Assessing Science Inquiry using MDP Goal Detectors

Michelle Lamar, Educational Testing Service

Janet Koster van Groos, Educational Testing Service

Abstract

Complex cognitive tasks, such as science inquiry, often involve a
sequence of goals, each of which is pursued through a sequence of actions.
Effective assessment of inquiry performance requires identification of these
student goals. Markov decision processes (MDPs) have been used to infer goals
and beliefs over a single directed sequence of actions (Baker et al., 2009), but
multi-goal complex systems are computationally prohibitive to model. This
research investigates the use of targeted MDPs as goal detectors, embedded within
a larger hidden Markov model (HMM) that accounts for the transition between
goals. This multi-layer approach allows the MDP state spaces to remain small
while modeling complex cognition. Because canonical HMM estimation is
complicated by the dynamic nature of MDPs, in which action probabilities depend
on context, we explore several different estimation methods. The approach is
applied to log-file data of test-taker interactions with a simulation-based
science inquiry assessment.